ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential Equations

Early diagnosis of disease can lead to improved health outcomes, including higher survival rates and lower treatment costs. With the massive amount of information available in electronic health records (EHRs), there is great potential to use machine learning (ML) methods to model disease progression aimed at early prediction of disease onset and other outcomes. In this work, we employ recent innovations in neural ODEs combined with rich semantic embeddings of clinical codes to harness the full temporal information of EHRs. We propose ICE-NODE (Integration of Clinical Embeddings with Neural Ordinary Differential Equations), an architecture that temporally integrates embeddings of clinical codes and neural ODEs to learn and predict patient trajectories in EHRs. We apply our method to the publicly available MIMIC-III and MIMIC-IV datasets, and we find improved prediction results compared to state-of-the-art methods, specifically for clinical codes that are not frequently observed in EHRs. We also show that ICE-NODE is more competent at predicting certain medical conditions, like acute renal failure, pulmonary heart disease and birth-related problems, where the full temporal information could provide important information. Furthermore, ICE-NODE is also able to produce patient risk trajectories over time that can be exploited for further detailed predictions of disease evolution

Protein multi-scale organization through graph partitioning and robustness analysis: Application to the myosin-myosin light chain interaction

Despite the recognized importance of the multi-scale spatio-temporal organization of proteins, most computational tools can only access a limited spectrum of time and spatial scales, thereby ignoring the effects on protein behavior of the intricate coupling between the different scales. Starting from a physico-chemical atomistic network of interactions that encodes the structure of the protein, we introduce a methodology based on multi-scale graph partitioning that can uncover partitions and levels of organization of proteins that span the whole range of scales, revealing biological features occurring at different levels of organization and tracking their effect across scales. Additionally, we introduce a measure of robustness to quantify the relevance of the partitions through the generation of biochemically-motivated surrogate random graph models. We apply the method to four distinct conformations of myosin tail interacting protein, a protein from the molecular motor of the malaria parasite, and study properties that have been experimentally addressed such as the closing mechanism, the presence of conserved clusters, and the identification through computational mutational analysis of key residues for binding.Comment: 13 pages, 7 Postscript figure

Forage Quality and the Environment

The influence of environmental factors on forage quality of temperate and tropical grasses has been reviewed by several authors, who summarized how light, temperature, drought and soil nutrients influence chemical composition, and digestibility of forages grown in contrasting areas of the world. The effects of season of the year on forage growth, grazing behavior and animal performance have also been the subject of numerous papers and reviews. However, there are few recent reviews that summarize how changes in climatic and edaphic factors influence forage quality of legumes with variable levels of condensed tannins (CT), which are important secondary compounds in some temperate and tropical legume species adapted to acid infertile soils. In this paper we summarize properties of CT and their positive and negative effects on forage quality of legumes. We also review published work on the effect of temperature, drought, CO2 concentration, season of the year and soil fertility on the accumulation of CT in temperate and tropical legumes. Results from experiments under controlled conditions indicate that high temperature alone can significantly increase the accumulation of CT in some temperate legume species (i.e. Lotus pedunculatus) but not in others (i.e. L. corniculatus). However, the effect of low or high temperature on accumulation of CT is considerably greater when accompanied with other environmental factors such as drought, high CO2 concentration and soil nutrient deficiencies. Soil nutrient deficiencies can have a major effect on elevation of CT concentration and overall feed value of temperate and tropical legumes, but only when deficiencies are such that they affect plant growth. Soil fertility and climatic conditions affect not only the concentration of CT but also their monomer composition and MW (molecular weight), as was observed in a tropical legume species well adapted to acid infertile soils. The nutritional significance of these findings are not all that well understood, but it would seem that CT in forage legumes are not a uniform chemical entity given that they can change with edaphic and climatic factors. Finally we suggest that there is a need to investigate alternatives to enhance the feed value of legumes with tannins adapted to acid soils through selection of genotypes with less CT and /or through manipulation of environmental factors such as soil fertility. For this we need to better understand how edaphic and climatic factors affect not only accumulation of CT but also their chemical structure and biological activity and relate these changes to forage intake, digestibility, N utilization, and, ultimately, to performance of ruminant animals

Do teams alleviate or exacerbate the extrapolation bias in the stock market?

Investigamos la manera en que los equipos influyen en la extrapolación de rentabilidades, un sesgo en la formación de creencias que es generalizado a nivel individual y crucial para los modelos conductuales de valoración de activos. Utilizando una muestra de gestores de fondos de inversión en acciones de EEUU, encontramos que los equipos atenúan el sesgo de extrapolación de sus propios miembros en un 75 %. Esta reducción no se debe al aprendizaje ni a diferencias en la remuneración, la carga de trabajo o los objetivos de inversión entre los fondos administrados individualmente y los administrados en equipo. En cambio, proporcionamos evidencias que apoyan la hipótesis de que la reducción del sesgo proviene de los miembros del equipo que participan en una reflexión cognitiva más profunda.We investigate how teams impact return extrapolation, a bias in belief formation which is pervasive at the individual level and crucial to behavioral asset-pricing models. Using a sample of US equity money managers and a within-subject design, we find that teams attenuate their own members’ extrapolation bias by 75%. This reduction is not due to learning or differences in compensation, workload, or investment objectives between solo-managed and team-managed funds. Rather, we provide supportive evidence that team members engaging in deeper cognitive reflection can explain the bias reduction

Listening to mental health crisis needs at scale: using Natural Language Processing to understand and evaluate a mental health crisis text messaging service

The current mental health crisis is a growing public health issue requiring a large-scale response that cannot be met with traditional services alone. Digital support tools are proliferating, yet most are not systematically evaluated, and we know little about their users and their needs. Shout is a free mental health text messaging service run by the charity Mental Health Innovations, which provides support for individuals in the UK experiencing mental or emotional distress and seeking help. Here we study a large data set of anonymised text message conversations and post-conversation surveys compiled through Shout. This data provides an opportunity to hear at scale from those experiencing distress; to better understand mental health needs for people not using traditional mental health services; and to evaluate the impact of a novel form of crisis support. We use natural language processing (NLP) to assess the adherence of volunteers to conversation techniques and formats, and to gain insight into demographic user groups and their behavioural expressions of distress. Our textual analyses achieve accurate classification of conversation stages (weighted accuracy = 88%), behaviours (1-hamming loss = 95%) and texter demographics (weighted accuracy = 96%), exemplifying how the application of NLP to frontline mental health data sets can aid with post-hoc analysis and evaluation of quality of service provision in digital mental health services

Comparative study of relationship between bruxism and decrease telomeres length

Poster presented at the First International Congress of CiiEM - From Basic Sciences To Clinical Research. Egas Moniz, Caparica, Portugal, 27-28 November 201

Linear models of activation cascades: analytical solutions and coarse-graining of delayed signal transduction

Cellular signal transduction usually involves activation cascades, the sequential activation of a series of proteins following the reception of an input signal. Here we study the classic model of weakly activated cascades and obtain analytical solutions for a variety of inputs. We show that in the special but important case of optimal-gain cascades (i.e., when the deactivation rates are identical) the downstream output of the cascade can be represented exactly as a lumped nonlinear module containing an incomplete gamma function with real parameters that depend on the rates and length of the cascade, as well as parameters of the input signal. The expressions obtained can be applied to the non-identical case when the deactivation rates are random to capture the variability in the cascade outputs. We also show that cascades can be rearranged so that blocks with similar rates can be lumped and represented through our nonlinear modules. Our results can be used both to represent cascades in computational models of differential equations and to fit data efficiently, by reducing the number of equations and parameters involved. In particular, the length of the cascade appears as a real-valued parameter and can thus be fitted in the same manner as Hill coefficients. Finally, we show how the obtained nonlinear modules can be used instead of delay differential equations to model delays in signal transduction.Comment: 18 pages, 7 figure

Synchronization in small-world systems

We quantify the dynamical implications of the small-world phenomenon. We consider the generic synchronization of oscillator networks of arbitrary topology, and link the linear stability of the synchronous state to an algebraic condition of the Laplacian of the graph. We show numerically that the addition of random shortcuts produces improved network synchronizability. Further, we use a perturbation analysis to place the synchronization threshold in relation to the boundaries of the small-world region. Our results also show that small-worlds synchronize as efficiently as random graphs and hypercubes, and more so than standard constructive graphs

Spontaneous creation of discrete breathers in Josephson arrays

We report on the experimental generation of discrete breather states (intrinsic localized modes) in frustrated Josephson arrays. Our experiments indicate the formation of discrete breathers during the transition from the static to the dynamic (whirling) system state, induced by a uniform external current. Moreover, spatially extended resonant states, driven by a uniform current, are observed to evolve into localized breather states. Experiments were performed on single Josephson plaquettes as well as open-ended Josephson ladders with 10 and 20 cells. We interpret the breather formation as the result of the penetration of vortices into the system.Comment: 5 pages, 5 figure